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arXiv · 1708.08142

Study of Set-Membership Kernel Adaptive Algorithms and Applications

R. C. de Lamare·André Flores
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Abstract

Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless these methods deal with kernel expansions, creating a growing structure also known as dictionary, whose size depends on the number of new inputs. In this paper we derive the set-membership kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is capable of limiting the size of the dictionary created in stationary environments. We also derive as an extension the set-membership kernelized affine projection (SM-KAP) algorithm. Finally several experiments are presented to compare the proposed SM-NKLMS and SM-KAP algorithms to the existing methods.

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R. C. de Lamare, André Flores. 2017-08-27. Study of Set-Membership Kernel Adaptive Algorithms and Applications. https://arxiv.org/abs/1708.08142

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